The development of a subsurface oil or gas field generally includes the placement of drilling platforms (or the use of existing platforms), as well as the placement of borehole trajectories and well completions. Determining the correct placement of wells during field development is a crucial step in exploration and production workflow. There are many elements to complicate this process. For example, the geology and geomechanics of the subsurface influence where wells can be placed efficiently and safely. The wells themselves have drilling and construction constraints, such as new wells must avoid existing wells. Constraints also exist at the surface: there may be bathymetric or topographic constraints, legal constraints, and constraints related to existing facilities such as platforms and pipelines. Also, the effects of financial uncertainty over time may impact the viability of different solution options.
A Shared Earth Model (SEM) is a geometrical and material property model of the subsurface for an oil and gas field. The model is shared in the sense that it integrates the work of several experts (geologists, geophysicists, well log analysts, reservoir engineers, etc.). Users can typically interact with the model through various application programs, such as the PETREL® software package offered by the assignee of the present application, Schlumberger Technology Corporation of Sugar Land, Tex. SEM information is often displayed as a three-dimensional, finite element map of the geological subsurface. Ideally, SEM contains all available information about a reservoir, and thus forms the basis to make forecasts and plan future actions. However, to a greater or lesser extent, uncertainty exists in SEM parameter values. While acquiring more measurements can reduce uncertainty, it is important to weigh the cost of data acquisition against the benefits of reducing uncertainty. Examples of physical variables in a Shared Earth Model (SEM) that are normally considered during the process of developing a Field Development Plan are listed below:
i. Reservoir geology                1. Stratigraphy (e.g. facies)        2. Structure (e.g. faults)        
ii. Reservoir petrophysics                1. Porosity        2. Saturation        3. Permeability        
iii. Reservoir Fluid Properties                1. Level of corrosive gases such as H2S        2. Hydrocarbon compositions        3. Hydrocarbon saturation pressures        4. Acidity of the water        
Of course, parameter variables can also relate to other aspects of the scenario, such as engineering (existing facilities and the need to avoid collision of new borehole trajectories with existing boreholes), operational (binding contracts, e.g., a contract to drill 20 wells per year), or financial (oil price, facility cost, well drilling, construction and production cost) aspects of the project.
Field Development Plans are normally designed in order to meet various objectives, for example, maximum net present value (NPV) from the oil or gas field, or maximum total production in a given period, or to achieve other goals. A typical Field Development Plan includes platform locations, well or borehole trajectories and capacity, completion type, location and flow rate, and reservoir simulator parameters, for example, oil or gas rate. As mentioned, the field development process requires the consideration of a wide variety of parameter variables which cannot be controlled and may be uncertain in nature, as well as a wide variety of constraints, such as physical, engineering, operational, and financial constraints which have to be accounted for in the final Field Development Plan. For example, there may be legal or physical reasons preventing a drilling platform from being constructed in a specific x-y location. Optimizing the field development decision making process is important because initial field production management strategies may impact the viability of the entire field over both the short and long term horizons.
The complexities in designing a Field Development Plan (FDP) lend themselves to mathematical optimization techniques. In this regard, automated or semi-automated Field Development Planning provides the promise of not only facilitating faster decision making, but also rendering the decision making more reliable inasmuch as candidate choices can be quantitatively evaluated and then selected or rejected. Thus, it is not surprising that there has been a long history of research associated with automated and semi-automated Field Development Planning.
Optimization of the Field Development Plan is a highly combinatorial and non-linear exercise. Early work was based on the mixed-integer programming approaches (Rosenwald et al. 1974; Beckner and Song, 1995; Santellani et al. 1998; Leraperititou et al. 1990). This work principally focuses on vertical wells and simplistic static models. Recently, much work has been published on a technique termed “the hybrid genetic algorithm” (HGA) to develop a Field Development Plan that supports non-conventional (non-vertical) wells and side tracks (e.g., Güyaguler et al. 2000; Yeten et al. 2002; Badra et al. 2003; Güyaguler and Horne 2004). While this technique is relatively efficient, the underlying well model is simplistic: a single well with one vertical segment down to a kickoff depth (heal), then an optional deviated segment extending to the toe. Yet, the sophistication of optimized Field Development Plans based on the hybrid genetic algorithm has grown in the past few years. For example, the time component has been included to support injectors, and uncertainty in the reservoir model is being considered (e.g., Cullick et al. 2003; Cullick et al. 2005).
One of the difficulties in developing a practical automated Field Development System has been the overwhelming computational resources required to accurately and completely model production from candidate Field Development Plans for a given oil or gas field. To date, therefore, systems to optimize the Field Development Planning process have been limited in their use.